RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source)

RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) After 20+ hours of compute time on local hardware, I benchmarked 7 RAG configurations against real-world tasks. SEQUOIA (RAPTOR tree + step-back prompting) consistently outperformed alternatives. The Full Pipeline List Method Core Approach No-RAG Direct LLM generation Classical RAG Dense retrieval (BGE-small + FAISS) Hybrid RAG BM25 + Dense + RRF + reranker LightRAG Key-value graph + dense hybrid PageIndex Two-stage hierarchical retrieval GraphRAG Entity graph + dense fallback Agentic RAG Multi-step reasoning pipeline SEQUOIA RAPTOR tree + step-back prompting SEQUOIA Pro Multi-query + rerank + compression Why LightRAG Underperformed The hype suggested graph-based RAG would revolutionize retrieval. On real banking documents and technical manuals: Graph construction is expensive (entity extraction, relationship mapping) Retrieval quality did not justify the overhead Academic benchmarks do not equal production reality Why RAPTOR Works Recursive Abstractive Processing for Tree-Organized Retrieval: Cluster leaf nodes (individual chunks) Summarize upward (hierarchical abstraction) Retrieve at multiple levels (specific details + high-level context) This mirrors how humans organize knowledge. Step-Back Prompting: Free Performance Before retrieving, generalize the query: User asks: "What's the error rate for Q3?" Step-back: "What metrics are tracked quarterly?" Retrieve broader context first, then narrow Result: ~15% improvement in recall. Zero latency cost. SEQUOIA Architecture User Query Step-back Prompting (generalize) RAPTOR Tree Retrieval (multi-level) Context Compression (summarize long contexts) Re-ranking (cross-encoder) Local LLM Generation Local LLM Evaluation I used a local model weaker than GPT-4 for judging. Key finding: relative rankings between methods stayed consistent even with a weaker evaluator. You can prototype and compare approaches without burning API credits on GPT-4 evaluations. Productio